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ES42CH14-Cahill ARI 7 October 2011 ANNUAL REVIEWS Further 15:40 Annu. Rev. Ecol. Evol. Syst. 2011.42:289-311. Downloaded from www.annualreviews.org by University of Alberta on 11/29/13. For personal use only. Click here for quick links to Annual Reviews content online, including: • Other articles in this volume • Top cited articles • Top downloaded articles • Our comprehensive search The Behavioral Ecology of Nutrient Foraging by Plants James F. Cahill Jr and Gordon G. McNickle∗ Department of Biological Sciences, University of Alberta, Edmonton AB T6G 2E9, Canada; email: [email protected] Annu. Rev. Ecol. Evol. Syst. 2011. 42:289–311 Keywords First published online as a Review in Advance on August 29, 2011 root ecology, plant behavior, plant-soil interactions, plant strategies, precision, resource selection functions The Annual Review of Ecology, Evolution, and Systematics is online at ecolsys.annualreviews.org This article’s doi: 10.1146/annurev-ecolsys-102710-145006 c 2011 by Annual Reviews. Copyright All rights reserved 1543-592X/11/1201-0289$20.00 ∗ Current address: Department of Biological Sciences, University of Illinois, Chicago, Illinois 60607; email: [email protected]. Abstract Foraging for resources influences ecological interactions among individuals and species, regardless of taxonomic affiliation. Here we review studies of nutrient foraging in plants, with an emphasis on how nutritious and nonnutritious cues in the soil alter behavioral decisions and patterns of root placement. Three patterns emerge: (a) Plants alter root placement in response to many diverse cues; (b) species respond differently to these cues; and (c) there are nonadditive responses to multiple cues, indicating that plants exhibit complex multidimensional root foraging strategies. We suggest that this complexity calls for novel approaches to understanding nutrient foraging by plants. Resource selection functions are commonly used by animal behaviorists and may be useful to describe plant foraging strategies. Understanding such approaches may allow researchers to link individual behavior to population and community dynamics. 289 ES42CH14-Cahill ARI 7 October 2011 15:40 INTRODUCTION Annu. Rev. Ecol. Evol. Syst. 2011.42:289-311. Downloaded from www.annualreviews.org by University of Alberta on 11/29/13. For personal use only. Behavior: we use Silvertown & Gordon’s (1989) definition of behavior, “what a plant or animal does, in the course of an individual’s lifetime, in response to some event or change in its environment” Foraging: the act of searching for, acquiring, and consuming resources Proximate and ultimate: the ethologist Nikolaas Tinbergen suggested that forms of questions for any behavior could be classified as: the proximate (i.e., evolutionary) mechanisms of adaptation and evolution Cue: a specific signal that can induce a behavioral response Behavioral type: within a genotype or species, a consistent behavioral response to a specific cue The idea that plants behave is well established in the literature (Hutchings & de Kroon 1994, Karban 2008, Lacey & Herr 2005, Marshall & Folsom 1991, McNickle et al. 2009, Silvertown & Gordon 1989). Among the best-studied aspects of plant behavior is that of root foraging for soil nutrients. Finding and acquiring resources are fundamental aspects of the ecology of organisms, and behaviorists have studied both proximate and ultimate (sensu Tinbergen 1963) aspects of foraging ecology (Stephens et al. 2007). Behaviorists recognize that individual decisions are influenced by genetic constraints as well as multiple local cues (e.g., resources, predators). Here we define the combined responses to multiple cues as an organism’s foraging strategy. Variation in foraging strategies among individuals and species can have consequences for population and community dynamics. To facilitate understanding, we provide a critical review of these behavioral aspects of plant foraging for nutrients. Plant root growth is plastic, resulting in nonuniform root distributions in soil. Some of this plasticity results from the plant’s condition (e.g., size, health), whereas some results from soil factors such as resource distributions or neighbor presence (Hodge 2004, Hutchings & de Kroon 1994, Kembel & Cahill 2005, Robinson 1996). Because resource capture influences the energetic and nutritive state of organisms, behaviorists recognize the importance of understanding foraging ecology (Stephens et al. 2007) in order to understand broader aspects of an organism’s life history and general ecology. Such a need for this understanding is just as strong for plants as for animals. Roots drive many ecological interactions and processes (e.g., Casper & Jackson 1997, Hutchings & de Kroon 1994, Wardle 2002). They can account for the majority of plant biomass in many communities ( Jackson et al. 1996), and a significant proportion of photosynthate is fed to fungi and bacteria that live in and around plant roots (Wardle 2002). Furthermore, competition for nutrients often reduces plant growth more than competition for light (e.g., Casper & Jackson 1997). The dynamics of competition experienced by individual plants can be influenced by the spatial distribution of roots among co-occurring plants. However, processes that determine where plants put their roots within the soil are poorly understood in comparison with analogous aboveground processes in plants (de Kroon & Hutchings 1995) and movement patterns among vertebrates. Here we review the behavioral ecology of plant root foraging for nutrients, linking local decisions to larger-scale impacts. We focus primarily on the behavioral aspects of root growth and placement, although many other aspects of nutrient foraging are also important (e.g., uptake kinetics). A review on this subject is timely, as we believe recent conceptual (de Kroon et al. 2009, Dudley & File 2007, Forde 2009, Gersani et al. 2001, Hodge 2009, McNickle & Cahill 2009, Novoplansky 2009) and technological (Table 1) advances will allow for rapid advancements in our understanding of plant foraging behavior. Here we provide a critical assessment of current knowledge to guide and stimulate future research. Specifically, we focus on two questions: 1. What soil factors influence where an individual plant places its roots in the soil? 2. How do root distribution responses to multiple factors combine to describe a plant’s overall foraging strategy? The first question is analogous to understanding where on a landscape a cougar hunts or an elk feeds as a function of resource distributions. There exists substantial interspecific variation in root placement relative to cues, suggesting adaptive benefits of different behavioral responses under different conditions (see below). Building on the terminology of Sih (2004b), we refer to consistent differences among individuals or species in response to specific cues as alternative behavioral types. In the first section of this review, we examine the evidence for alternative behavioral types in response to numerous nutritious and non-nutritious cues. 290 Cahill · McNickle ES42CH14-Cahill ARI 7 October 2011 15:40 Table 1 Comparison of some methods used to study root distributions and species identification, along with representative references Annu. Rev. Ecol. Evol. Syst. 2011.42:289-311. Downloaded from www.annualreviews.org by University of Alberta on 11/29/13. For personal use only. Method Description Advantages Disadvantages Excavation Whole root systems or plots are excavated by digging Generates large amounts of high-quality data; provides identification to the level of the individual and species Often misses fine roots destroyed by digging; extremely laborious and impractical for experiments with many treatments or substantial replication Brisson & Reynolds 1994 Reference(s) Root chambers, rhizotrons, and minirhizotrons Roots are observed/ photographed through clear material sunk in the ground, in rooting chambers, or through tubes buried in soil Adaptable to greenhouse and field studies; can be inexpensive Measures a tiny fraction of the root volume, in two dimensions; hard to identify roots to the individual or species; time-consuming image analysis Fransen & de Kroon 2001, McNickle & Cahill 2009, Mommer et al. 2010 Staining Colored dye is injected into the root system of focal plants, with identification based on color; can be used in combination with imaging Inexpensive Difficult to get reliable and even staining of roots; labor intensive Cahill et al. 2010, Schenk et al. 1999 Fluorescence and reflectance Roots are exposed to different wavelengths of light; differential fluorescence (ultraviolet) or reflectance indicates species identity Can allow for determination of relative abundance; once equipment purchased, inexpensive Poorly understood and highly species/system specific; time-consuming image analysis Caldwell et al. 1991a, Roumet et al. 2006 Tracers Rare elements or compounds are injected into soil; shoots are analyzed, and the positioning of roots is inferred from elevated shoot concentrations Can provide identification to the level of the individual or species; relatively simple Low number of tracers restrict the number of soil locations measured; does not differentiate between root and fungal uptake; chemical analyses are relatively expensive Casper et al. 2000 Morphological identification Roots are collected and identified based on morphological differences Can provide identification to the level of species; inexpensive Intraspecific variation in root morphology limits interspecific comparisons; identification of individuals is not possible; timeconsuming Gasson 1979 Molecular identification Species are determined from root tissue on the basis of DNA markers Can provide identification to the level of individual or species; increasingly inexpensive Requires expertise with molecular techniques; high-throughput methods still being developed; typically results in presence/absence data, not relative abundance Bobowski et al. 1999, Brunner et al. 2001, Jackson et al. 1999, McNickle et al. 2008, Moore & Field 2005, Ridgway et al. 2003, Taggart et al. 2010 (Continued ) www.annualreviews.org • Plant Foraging Behavior 291 ES42CH14-Cahill ARI 7 October 2011 15:40 Table 1 (Continued ) Method Annu. Rev. Ecol. Evol. Syst. 2011.42:289-311. Downloaded from www.annualreviews.org by University of Alberta on 11/29/13. For personal use only. Quantitative polymerase chain reaction (PCR) Description Similar to molecular identification, except quantitative real-time PCR is used to identify species and estimate relative abundance Resource selection function (RSF): any statistical model that gives the probability that a unit of habitat will be used by an organism based on biotic or abiotic variables associated with each habitat unit Advantages Can provide species-specific abundance and distribution data from samples Disadvantages Requires a reliable internal standard to obtain useful quantitative PCR data; limited to a low number of species per sample; expensive Reference(s) Mommer et al. 2008, 2010 The second question is concerned with the effects of multiple cues on root distributions. Behavioral types may not be an additive combination of responses to isolated cues, and therefore we discuss integrative nutrient foraging strategies. We introduce resource selection functions (RSFs), a statistical approach often used to model habitat selection and describe patterns of occupancy by foraging animals, as one potential tool to describe root placement in response to multiple cues. Such information is critical to inferring and defining alternative nutrient foraging strategies among individuals and species. Finally, we present a conceptual overview of how plant foraging for nutrients can be integrated into existing frameworks linking plant traits to larger-scale processes. We also highlight specific areas we believe are the highest priorities for additional research in the general area of plant foraging. WHAT SOIL FACTORS INFLUENCE WHERE AN INDIVIDUAL PLANT PLACES ITS ROOTS IN THE SOIL? Since the work of Drew (1975) and Drew et al. (1973), it has become apparent that nutrients serve as cues influencing root growth and placement. Consequently, root system architecture is highly dynamic, developing partially in response to the spatial distribution of nutrients in the soil (e.g., Hodge 2004). However, roots also respond to non-nutritious cues in the soil, such as soil biota and neighboring plants (e.g., Cahill et al. 2010, Stevens & Jones 2006). Combined, this evidence suggests that plants exhibit fine-scale variation in lateral and vertical root distributions, driven in large part by behavioral response to diverse soil cues. In this section we review the different cues that may impact root placement in soil and the alternative behavioral types that exist among species. However, we begin by emphasizing that not all variation in root growth and placement is necessarily the outcome of behavior. Stochastic Influences on Root Development Genetically identical plants grown under environmentally uniform conditions do not necessarily produce identical patterns of root placement; instead they may exhibit stochasticity in root placement (reviewed in Forde 2009). This random variation in root development has been called developmental instability (Forde 2009), and its effects on root architecture can be substantial, even surpassing variation induced by environmental cues (Forde 2009). The degree to which a plant’s root system architecture is influenced by stochastic processes has a genetic component, suggesting that natural selection may shape developmental instability (Forde 2009). The potential benefits of random processes as part of a nutrient foraging strategy are not known, although it may reduce intraplant root competition and increase the probability of finding localized nutrient patches (Forde 2009). Animal behaviorists have developed an extensive literature 292 Cahill · McNickle ES42CH14-Cahill ARI 7 October 2011 15:40 focusing on the costs and benefits of alternative search strategies that incorporate varying degrees of stochasticity in searching (Smouse et al. 2010). Analogous research linking root placement and stochastic search processes is lacking. Differentiating among stochastic and behavioral causes of root placement patterns remains a challenge. The spatial distribution of soil nutrients varies at scales smaller ( Jackson & Caldwell 1993) and larger than the size of a single plant. Here we focus on variation at scales within the soil potentially explored by an individual plant, as this is most relevant for behavioral decisions. The effects of resource distributions on root placement have been reviewed elsewhere (e.g., de Kroon et al. 2009; Hodge 2004, 2006, 2009; Hutchings & de Kroon 1994; Kembel & Cahill 2005; Reynolds & Pacala 1993), and thus we focus on a behavioral interpretation of these responses. When nutrients are heterogeneously distributed in soil, plants generally place more roots into locations with higher nutrient concentrations (Hodge 2004, Kembel & Cahill 2005). This behavioral response has been referred to as root proliferation and foraging precision (Hodge 2004, Kembel & Cahill 2005). We prefer the latter term, as it focuses on the general behavior, rather than a developmental response. For example, foraging precision can be expressed developmentally either through increased root growth (e.g., Bilbrough & Caldwell 1995) or through decreased root mortality (Gross et al. 1993), but the resulting behavior is identical. Although most species alter root placement in response to nutritious cues (Kembel & Cahill 2005), there is substantial interspecific variation (Figure 1). Some species place a large proportion of roots in nutrient patches, exhibiting the behavioral type of high precision (sensu Campbell et al. 1991). Other species have a more muted response to the same resource cues (Figure 1), displaying the behavioral type of low precision. Some variation in behavioral type is phylogenetically 1.0 0.9 0.8 Precision Annu. Rev. Ecol. Evol. Syst. 2011.42:289-311. Downloaded from www.annualreviews.org by University of Alberta on 11/29/13. For personal use only. Root Responses to Nutritious Cues 0.7 Monocots 0.6 0.5 0.4 Eudicots 0.3 0 20 40 60 80 100 120 Rank Figure 1 Rank order of 120 species’ foraging precision measured as the proportion of fine roots in nutrient-enriched patches (0.5 = random root growth with respect to nutrient distributions; 1.0 = all roots are located in nutrient-enriched patches). Monocots are indicated by dark green circles and eudicots by light green circles. Data are from Kembel & Cahill (2005). www.annualreviews.org • Plant Foraging Behavior 293 ARI 7 October 2011 15:40 conserved, as eudicots generally have higher precision compared with monocots (Kembel & Cahill 2005). Nutritious cues also influence uptake kinetics (Fransen et al. 1999) and root demography (Gross et al. 1993), although these are not as well studied, and we do not discuss them here. An additional limitation in the available data is our lack of understanding functional and behavioral differences due to distributions of different forms (e.g., NO3 versus NH4 ) and types of nutrients (e.g., nitrogen versus phosphorus), as well as differences among soil nutrients and water. Existing studies tend to either manipulate resources as a group by adding NPK or organic fertilizers, or they tend to manipulate a single nutrient, without comparing responses to other nutrients. Such tendencies may reflect the chemistry of natural nutrient patches (e.g., urine patches) and yield the most useful ecological information, but they also limit the ability to draw conclusions about potential behavioral responses to specific compounds. In addition to changes in root placement, nutritious cues can influence the transient processes of root growth. For example, consistent with a prediction of the marginal value theorem (Charnov 1976), the presence of nutrient-rich patches can cause plants to reduce their rate of soil exploration such that they leave nutrient-rich patches later than they leave nutrient-poor patches (McNickle & Cahill 2009). Plants can also respond to nutrient patches through reduced root mortality (Gross et al. 1993), also resulting in a pattern of increased residency in nutrient-rich patches relative to nutrient-poor patches. Few species have been screened for the transient processes described here, and as a result, the generality of these behaviors and potential alternative behavioral types are unknown. At the individual level, it is critical to know which specific traits directly influence resource capture and may be subject to optimization. Several cost-benefit approaches to understanding root growth have been proposed, including a focus on the resource capture efficiency of individual roots (Eissenstat et al. 2000) and whole-plant fitness as a function of alternative allocations to individual nutrient foraging strategies (McNickle et al. 2009). However, data explicitly testing these possibilities are lacking. Instead, there has been a general assumption that root biomass distributions are reflective of functionally important behavioral decisions. This assumption has generally not been challenged with data. Annu. Rev. Ecol. Evol. Syst. 2011.42:289-311. Downloaded from www.annualreviews.org by University of Alberta on 11/29/13. For personal use only. ES42CH14-Cahill Molecular Mechanisms of Root Placement Most of our understanding of the genes that govern root placement comes primarily from studies of Arabidopsis thaliana. As a result, the molecular basis to explain the breadth of alternative behavioral types known to exist (Figure 1) is lacking. Root placement can be influenced by root-level responses to the local conditions and, under some circumstances, systemic modulation of the response depending on the whole-plant status (Bilbrough & Caldwell 1995, Cui & Caldwell 1997, de Kroon et al. 2009). Genes that act as nitrate sensors and multigene pathways that influence both root growth rate and the production of secondary lateral roots have been described (Chen et al. 2008, Chrispeels et al. 1999, de Kroon et al. 2009, Forde 2009, Forde & Walch-Liu 2009). These mechanisms are partially involved in plant responses to nutritious cues (nitrate patches). However, the molecular biology of root proliferation is complex (Forde & Walch-Liu 2009) and involves more root-specific and generalized physiological pathways than are currently understood (de Kroon et al. 2009). More thorough reviews on this topic have been provided elsewhere (Chen et al. 2008, Chrispeels et al. 1999, de Kroon et al. 2009, Forde 2009, Forde & Walch-Liu 2009). Responses to Non-Nutritious Cues Soil contains more than nutrients and is also home to competitors, pathogens, mutualists, and herbivores. This is the ecological context in which plant root foraging behavior evolved 294 Cahill · McNickle ES42CH14-Cahill ARI 7 October 2011 15:40 (Hodge 2001, Hodge et al. 2000b, Robinson et al. 1999, Stevens & Jones 2006), and it is expected that root distributions reflect not only resource distributions but also the distributions and pressures of these other factors. Although responses to non-nutritious cues are relatively understudied, in the subsequent sections we review what is known about the impacts of neighbors, mutualisms, and predation/pathogens on root placement by plants. Annu. Rev. Ecol. Evol. Syst. 2011.42:289-311. Downloaded from www.annualreviews.org by University of Alberta on 11/29/13. For personal use only. Root Placement Responses to Neighboring Plants Competition typically reduces the total amount of roots that plants have available to place in soil (Casper & Jackson 1997, Goldberg et al. 1999) as well as the fine-scale distribution of roots that are produced (Novoplansky 2009). Litav & Harper (1967) suggested three ways that competition may influence the spatial distribution of the roots of competing plants: random mixing, undermixing, and overmixing. Which pattern occurs depends on each plant’s response to roots of con- and heterospecific neighbors. Building on the concepts of Litav & Harper (1967), we suggest there are three behavioral types that may occur in response to the presence of neighbors: no response, avoidance, and aggregation. With the behavioral type of no response, plant root placement would be constant (relative to shoot size) regardless of the presence or identity of neighboring roots. Importantly, such a pattern can occur either because a plant is unable to detect/respond to neighbors or because plants do respond to neighboring roots but do not differentiate between their roots and those of different individuals in the soil. If all co-occurring individuals demonstrate this behavioral type, then random mixing of roots should occur at the plot level (sensu Litav & Harper 1967). This behavioral type has been documented (Litav & Harper 1967, Mommer et al. 2010). However, in a study involving five species, Litav & Harper (1967) found that two species expressed no response under low-nutrient conditions and expressed avoidance under high-resource conditions. This switch in behavioral type emphasizes a recurrent theme in this review: The behavioral type expressed by an organism in response to one cue is often contingent on the presence or absence of other cues in the soil environment. Aggregation occurs if plants increase root growth in the vicinity of neighbor roots. This idea, also referred to in the literature as over-proliferation (sensu Gersani et al. 2001), has been formalized for annual plants with simple game theoretic models that assume plants compete only for resources by increasing root biomass. The resulting evolutionary stable strategy is the behavioral type of aggregation, even when this increased root production comes at the cost of root growth in unoccupied soil and reduced fitness (Gersani et al. 2001; O’Brien & Brown 2008; O’Brien et al. 2005, 2007). Many empirical studies that claim to demonstrate aggregation (e.g., Gersani et al. 2001, Maina et al. 2002, O’Brien et al. 2005) have confounded soil volume and soil fertility in their experimental designs, resulting in sharp criticism (Hess & de Kroon 2007, Laird & Aarssen 2005, Schenk 2006). Although this critique has been contested (O’Brien & Brown 2008), data are limited. However, if the behavioral type of aggregation occurs, overmixing of roots should occur in locations of root overlap among neighboring plants. Depending on compensatory responses elsewhere in the root system, overmixing, undermixing, or random root distributions could be found at the whole-plot level. The third behavioral type expressed in response to neighbor roots is avoidance, which results in the undermixing (i.e., segregation) of roots. Schenk et al. (1999) identified 13 species of plants from 16 studies that exhibited some degree of root segregation, with additional examples found in more recent studies (e.g., Cahill et al. 2010, Gersani et al. 1998, Holzapfel & Alpert 2003, von Felten & Schmid 2008). However, undermixing can occur both through avoidance and through the suppression of root growth of one plant by another (e.g., allelopathy) (Mahall & Callaway 1991). www.annualreviews.org • Plant Foraging Behavior 295 ES42CH14-Cahill ARI 7 October 2011 15:40 Table 2 A summary of the environmental cues discussed in this review and how they influence individual root foraging behaviors in plantsa Annu. Rev. Ecol. Evol. Syst. 2011.42:289-311. Downloaded from www.annualreviews.org by University of Alberta on 11/29/13. For personal use only. Effect on root placement Species studied Reference(s) Developmental instability Genetically identical plants may place roots differently owing to developmental instability Arabidopsis thaliana, Zea mays Reviewed in Forde 2009 Resources Plants generally preferentially place roots into nutrient-rich patches >120 species (see Kembel & Cahill 2005 for an extensive list) Reviewed in Hodge 2004, 2006, 2009; Hutchings & de Kroon 1994; Kembel & Cahill 2005; Robinson 1996 Plant neighbors No response: Plants do not adjust root placement in response to a neighbor Pisum sativum, Avena sativa (N-poor soil only), Leucanthemum vulgare, Plantago lanceolata Litav & Harper 1967, Mommer et al. 2010 Aggregation: Plants increase root growth near a neighbor; often called overproliferation Glycine max, Phaseolus varigaris, P. sativum, Anthoxanthum odoratum, Festuca rubra Gersani et al. 2001, Maina et al. 2002, Mommer et al. 2010, O’Brien et al. 2005 Avoidance: Plants reduce growth near a neighbor; one of several mechanisms leading to root segregation Pisum sativum, A. sativa (N-rich soil only), Cakile edentula, Abutilon theophrasti, and 13 more species from 19 papers reviewed in Schenk et al. (1999) Cahill et al. 2010, Dudley & File 2007, Gersani et al. 1998, Litav & Harper 1967, Schenk et al. 1999 Microbe neighbors Too few data Other mutualists Too few data Soil enemies Too few data a Table limited to studies cited in the main text. These are fundamentally different responses and highlight that the description of root patterns in soils cannot by itself indicate the specific behaviors expressed by co-occurring plants. The current evidence in the literature seems to suggest that avoidance may be the most common behavioral type in response to neighbor cues (Table 2). However, we caution that it is usually not clear in these studies whether avoidance is caused by behavioral shifts of one plant in response to another or whether it is an outcome of chemically mediated interference competition. Furthermore, the literature also shows that all three behavioral types do exist and that an individual species may express multiple behavioral types depending on other cues. Unfortunately, although many studies document patterns of root placement among cooccurring plants (e.g., Table 2), few studies directly observe changes in the root growth of one plant in response to roots of another (e.g., Cahill et al. 2010, Mahall & Callaway 1991, Mommer et al. 2010). Such studies are logistically challenging but are critical to draw conclusions about the specific behaviors plants express, rather than simply describing the pattern of root distributions that are the outcome of behavioral changes. We anticipate that recently developed molecular methods should facilitate this type of research (Table 1). Kin and Self-Recognition Another factor that can influence behavioral responses to neighboring roots is the relatedness of the potential competitor. For example, simulations of root system development typically include parameters that minimize the potential for overlap among intraplant (complete genetic similarity) roots (e.g., Lynch et al. 1997). Such decisions are supported by empirical observation, as 296 Cahill · McNickle ES42CH14-Cahill ARI 7 October 2011 15:40 plants show differential patterns of root placement in response to neighboring roots dependent on whether the neighbors are part of the same connected genet or whether connections have been severed (Falik et al. 2003, Gruntman & Novoplansky 2004). There is also evidence for kin recognition by plant roots (Dudley & File 2007), suggesting that a plant could enhance inclusive fitness by minimizing competition with close relatives (Hamilton 1964a,b). For example, Dudley & File (2007) found that Cakile edentula reduced competition with kin through the production of less fine root biomass compared with when plants competed with unrelated individuals. Similar results have been found for Impatiens pallida (Murphy & Dudley 2009). More studies are needed to understand the generality of these responses. Annu. Rev. Ecol. Evol. Syst. 2011.42:289-311. Downloaded from www.annualreviews.org by University of Alberta on 11/29/13. For personal use only. Root Placement Responses to Microbial Competitors Plants compete with fungi and bacteria for soil nutrients (e.g., Schimel & Chapin 1996). Furthermore, microbial community composition can demonstrate substantial variation at scales smaller than an individual root system (Franklin & Mills 2003), suggesting that the effects of plantmicrobial interactions also vary at small spatial scales (Hodge et al. 2000a). Thus we would expect that microbial distributions would be another cue influencing root placement, perhaps leading to behavioral types of no response, aggregation, and avoidance with respect to microbes. However, limited data exist that describe the fine-scale dynamics of plant-microbe competition (Hodge et al. 2000a), particularly in the context of plant behavioral responses. Given the frequency with which plant-microbe interactions occur, this is an area that requires attention. Root Placement Responses to Mutualists Symbioses with mycorrhizal fungi (and to a lesser extent nitrogen-fixing bacteria) are common among plants. These associations are generally viewed as mutualisms yielding nutrient benefits to the plant at the expense of carbon to feed the microbes (Smith & Read 1997). However, these interactions can vary from parasitism to mutualism as a function of nutrient conditions ( Johnson et al. 1997). Consequently, there is reason to expect that mycorrhizal interactions will alter aspects of plant nutrient foraging behavior. A handful of studies have measured whether mycorrhizae alter a plant’s foraging precision in response to nutritious cues. For example, Hodge (2001) grew Plantago lanceolata in soil with heterogeneous soil nutrients, both with and without arbuscular mycorrhizal fungi (AMF). Although plants increased root births when AMF were present, foraging precision (as measured as root length inside a nutrient patch) was unaffected. Similarly, Wijesinghe et al. (2001) found that none of six herbaceous species altered foraging precision as a function of plant mycorrhizal status. However, we caution that more experimentation is required that integrates nutrient foraging and mycorrhizal status, particularly for aspects of foraging other than precision. Even if foraging precision is unaltered by mycorrhizal colonization, plants infected by mycorrhizal fungi tend to decrease root length and root density at the level of the whole plant (e.g., Cui & Caldwell 1996, Hetrick 1991, Schroeder & Janos 2005). Such responses indicate that conventional measures of nutrient foraging (e.g., biomass allocation) are influenced by mycorrhizal status, even if other aspects of nutrient foraging (e.g., precision) are not. Mycorrhizal associations are often described as mutualisms that are less costly to plants than the production of fine roots (Fitter 1991) and in which fungal hyphae serve as simple extensions of plant roots (Cui & Caldwell 1996, Tibbett 2000). However, mycorrhizal fungi are not simply root extensions, but are complex organisms that are themselves shaped by natural selection (Helgason & Fitter 2009). These fungi acquire their own mineral nutrition through their own foraging behavior (Hodge & Fitter 2010), and we cannot assume that behavioral shifts in plants are necessarily to the www.annualreviews.org • Plant Foraging Behavior 297 ES42CH14-Cahill ARI 7 October 2011 15:40 benefit of the infected host plant. We suggest that viewing mycorrhizae as examples of reciprocal parasitism (e.g., Egger & Hibbett 2004) rather than root extensions may result in novel insight into plant foraging behavior. Under this model, there is explicit recognition of conflicts of interest among the partners engaged in a potential mutualism and that selection acts on individuals, and not on the interaction as a whole. Adapting this view may help in the identification of similarities in behavioral responses to putative mutualists, as well as traditional root parasites (described below). Root Placement Responses to Enemies Annu. Rev. Ecol. Evol. Syst. 2011.42:289-311. Downloaded from www.annualreviews.org by University of Alberta on 11/29/13. For personal use only. Root enemies consist of herbivores and pathogens/parasites from a variety of taxa and have the potential for direct and indirect effects on root growth (Wardle 2002). Differential patterns of root placement can occur (a) if root herbivores show preference for which roots they consume or (b) if plants alter root growth as a function of the distribution of enemies in the soil. As root enemies feed, they can consume roots and reduce the local abundance of roots, altering root distributions (Stevens & Jones 2006, Stevens et al. 2007). For example, if root herbivores demonstrate a numerical response to root densities (e.g., distribute according to an ideal free distribution), their feeding should homogenize root distributions within the soil. However, it will be difficult to separate the effects of plants altering root placement from the effects of soil enemies consuming roots, thereby altering locations that contain roots. How plants modify root placement in response to soil enemies is poorly understood. Plants can detect belowground enemies, although what plants do with that information is not always clear. For example, Zea mays is capable of detecting the root herbivore Diabrotica virgifera virgifera, responding by releasing (E)-β-caryophyllene and thereby attracting predatory nematodes (Rasmann et al. 2005). However, critical to understanding behavioral responses to soil enemies are data documenting the dynamics of root growth. Unfortunately, most studies involving soil enemies (Table 2) focus on total root biomass at the end of the experiment, a pattern caused either by behavioral decisions of the plant or by consumption of the plant by the herbivore/pathogen. Once again, we suggest that measures of the transient aspects of root growth and placement are needed to understand the behavior of plant root foraging. HOW DO ROOT DISTRIBUTION RESPONSES TO MULTIPLE FACTORS COMBINE TO DESCRIBE A PLANT’S OVERALL FORAGING STRATEGY? Two patterns emerge in how root placement is influenced by nutritious and non-nutritious cues: (a) Root placement is altered by multiple environmental cues (Table 2), and (b) plant species are variable in the behavioral types they exhibit (Figure 1 and Table 2). However, behavioral responses to multiple environmental cues can be nonadditive, generating complex multidimensional behavioral strategies. Consequently, the population- or community-level consequences of a particular soil factor (e.g., soil nutrient heterogeneity), mediated through behavioral responses, are context dependent. Conditionality of Behavior Behavioral types expressed in response to some soil cues (e.g., soil enemies, mutualists) have received limited attention. Consequently, the effects of multiple, simultaneous cues on root placement are generally difficult to predict. However, we can draw from the concepts of trade-offs and constraints to provide a context in which to begin to understand the available information. At a basic level, if a plant’s response to multiple cues were additive, we would expect to find consistent effects of those cues across different conditions (i.e., no statistical interaction in a factorial experiment). 298 Cahill · McNickle Annu. Rev. Ecol. Evol. Syst. 2011.42:289-311. Downloaded from www.annualreviews.org by University of Alberta on 11/29/13. For personal use only. ES42CH14-Cahill ARI 7 October 2011 15:40 The best-studied examples of plant foraging responses to multiple cues come from experimental manipulations of resource distributions and neighbors (Hutchings et al. 2003). If plant responses to nutritious cues are constant, soil nutrient heterogeneity might be expected to have consistent additive effects on larger-scale measures, such as population biomass, regardless of the levels of other factors (e.g., density of neighboring plants). Such consistency is not apparent, indicating context dependency on the expression of behavioral type (i.e., statistical interaction). For example, in two separate studies, Cardamine hirsuta was grown under varied conditions, including (a) different levels of soil nutrients, (b) varying levels of nutrient heterogeneity, and (c) different growth durations (Day et al. 2003a,b). Under one set of conditions (intermediatesized nutrient patches, plants grown for a short duration), population growth in heterogeneous soil was 33% less than in homogeneous soils. Under another set of conditions (low nutrients, large patches), population growth in heterogeneous soil was 250% greater than in homogeneous soil. Context dependency is also found in two studies with Abutilon theophrasti. In these studies, nutrient heterogeneity reduced population biomass 17% relative to homogeneous controls under one set of conditions (intermediate density, small and infrequent patches) but increased productivity by 44% under another combination of conditions (intermediate density, small and frequent patches) (Casper & Cahill 1996, 1998). Clearly, nutrient heterogeneity may have nonadditive effects on population growth, even within a single species. Underlying these varied responses of populations to soil nutrient heterogeneity are changes in individual plant responses to soil cues, and these individual responses are also nonadditive. For example, Cahill et al. (2010), also working with Abutilon theophrasti, combined root staining with images from a minirhizotron camera to directly observe root distributions in relation to both the location of soil patches and the presence or absence of neighbors. Under homogeneous nutrient conditions, this species exhibited strong root avoidance. However, soil nutrient heterogeneity reduced the magnitude of root avoidance, resulting in the overlapping of neighboring roots within nutrient-rich patches (Cahill et al. 2010). Other work on A. theophrasti using nonradioactive tracers (Table 1) indirectly showed that plants accessed resources at greater distances under heterogeneous than homogenous conditions (Casper et al. 2003). If root distributions alter individualand population-level resource uptake and growth, these shifts in behavioral responses to soil cues likely contributed to the complex population-level responses seen in other studies (e.g. Casper & Cahill 1996, 1998). Although less well studied, there is also evidence of conditional responses to non-nutritious cues. For example, Semchenko et al. (2007) observed the altered root placement of two species in response to a neighbor as a function of the species identities of both the focal and neighbor plant. They found that neighbor identity had no effect on Glechoma hederacea roots, whereas Fragaria vesca roots were stimulated by inter- but not intraspecific neighbors. There is also evidence that the benefits of mycorrhizal colonization can be most strongly expressed when plants compete for soil resources with other plants, rather than when grown individually (Hodge 2003). Thus, although there are few studies, a contingent behavioral response when presented with multiple soil cues appears to be common. This indicates a need for a multi-cue approach to understand plant foraging behavior and the resulting consequences. Using Theory to Understand Plant Foraging Behavior In the face of such empirical complexity, it would be helpful to turn to plant-based theory for a foundation on which to rest the contrasting results. However, the study of plant behavior has been primarily driven by empiricists rather than theoreticians. This has resulted in a situation in which the theoretical aspects of plant behavior are relatively underdeveloped both www.annualreviews.org • Plant Foraging Behavior 299 ES42CH14-Cahill ARI 7 October 2011 15:40 Annu. Rev. Ecol. Evol. Syst. 2011.42:289-311. Downloaded from www.annualreviews.org by University of Alberta on 11/29/13. For personal use only. in comparison with that of animal behavior and in comparison with the number of existing experiments. The most extensive work in plant behavioral theory relates to clonal plants and the optimal placement of daughter plants in a variable environment (de Kroon & Hutchings 1995). For example, using simulation modeling, Sutherland & Stillman (1988) described how soil conditions should favor particular behavioral types, such as reducing internodal length in areas of high-resource availability. Simulation modeling of nonclonal plants has also been used to show potential benefits of root responses to localized nutrient patches ( Jackson & Caldwell 1996, Ryel & Caldwell 1998). Similarly, de Kroon & Hutchings (1995) recognized the linkages between plant modularity and the fine-scale placement of roots and emphasized the need to develop a foraging theory that could apply to all plants. The identification of plant modularity as critical to understanding how a plant interacts with its environment is old (White 1979); however, the importance of this insight is essential for the development of any theory for plant foraging behavior (de Kroon et al. 2005, 2009; McNickle et al. 2009). Some studies have combined animal-derived theory to generate and test qualitative predictions about root placement in soil. For example, root placement in response to nutritious cues has been placed in the theoretical framework of optimal patch-use models, such as the marginal value theorem (Gleeson & Fry 1997, McNickle & Cahill 2009, McNickle et al. 2009). Similarly, alternative responses to neighbor roots have been addressed as a competitive game (Gersani et al. 2001; O’Brien & Brown 2008; O’Brien et al. 2005, 2007), viewed as a problem of kin selection (Dudley & File 2007, Murphy & Dudley 2009), and discussed in the context of ideal free distributions (Gersani et al. 1998). An alternative to game-theoretic approaches is that of swarming, with individual root tips acting as individuals (Baluska et al. 2010). Despite these examples, there have been few attempts to generate theory about how multiple behaviors may interact to influence whole-plant foraging strategies, individual fitness, and consequences at the population and community levels. Instead, we are left with a (small) number of theoretical works devoted to single behavioral types, on one hand, and a (small) number of empirical studies that demonstrate that the behavioral type expressed by a plant can be contingent on multiple cues in the soil, on the other hand. We suggest that a critical missing piece in the literature is a conceptual understanding of how individual behaviors combine to form behavioral strategies, along with the related plant-specific theory. Developing Foraging Strategies Root foraging strategy: the combination of behavioral types expressed by an individual plant in response to multiple cues related to root foraging 300 Here we define a plant’s nutrient foraging strategy as the combination of behavioral types expressed by an individual plant in response to multiple cues related to nutrient foraging. For example, how a plant responds to nutrient heterogeneity (degree of precision) defines one behavioral type (Figure 1). How that same individual responds to neighbors (no response, avoidance, aggregation) defines a second behavioral type (Table 2). That individual’s foraging strategy is defined by the combination of these behavioral types (e.g., avoidance + high precision), along with behavioral types induced by other cues (e.g., mutualists, enemies) and any contingencies in behavioral expression (e.g., avoidance only in homogeneous soils). Although there have yet to be explicit efforts to describe a plant’s overall nutrient foraging strategy, the idea that nutrient foraging behaviors should be integrated into a general understanding of interspecific variation in life-history strategies has been previously suggested. For example, Grime et al. (1997) included foraging precision as one variable describing a plant’s overall lifehistory strategy. Foraging precision (measured as the percent of root biomass in poor soil patches) covaried with 11 of the 53 traits measured and was heavily weighted on the second axis explaining Cahill · McNickle Annu. Rev. Ecol. Evol. Syst. 2011.42:289-311. Downloaded from www.annualreviews.org by University of Alberta on 11/29/13. For personal use only. ES42CH14-Cahill ARI 7 October 2011 15:40 trait variation among species (Grime et al. 1997). In a smaller analysis of 16 North American grassland species, Kembel et al. (2008) investigated correlations among a number of plant traits, including foraging precision and the tendency of a plant to acquire growth benefits from heterogeneous soil (relative to homogeneous soil). They too found that foraging precision was correlated with a number of functional traits, particularly those associated with a weedy life-history strategy (e.g., high leaf nitrogen and growth rates) (Kembel et al. 2008). Interestingly, there is little evidence to date that foraging precision itself is correlated with plant growth in heterogeneous soil, relative to growth in homogeneous soil (Kembel & Cahill 2005, Kembel et al. 2008). However, these data come only from isolated plants under artificial conditions, and growth benefits may only be obvious when plants compete for patches (Hodge et al. 1999, Robinson et al. 1999). Furthermore, such bivariate correlations do not necessarily reflect the more complex nutrient foraging strategies and conditions in which such benefits may be expressed. To understand whether nutrient foraging strategies, rather than single behaviors, better explain plant growth under diverse soil conditions, one requires methods to quantify and describe multidimensional behavioral strategies. Methods Used to Understand Correlated Behaviors Animal ecologists have addressed the need to account for multiple behaviors in several ways, and application of their methods may help in the study of plants. One approach focuses directly on correlations and contingencies among behaviors: behavioral syndromes. These can be described as suites of correlated behaviors displayed by individuals or species that are consistent across multiple situations (sensu Sih et al. 2004a). Behaviorists can use a direct approach to quantify foraging strategies by measuring different behavioral types within a single individual and can test for correlations using a phenotypic selection approach (Dingemanse et al. 2002). Although such studies typically are done within a species, interspecific comparisons are possible (Sih et al. 2004a,b). Such comparisons would allow researchers to begin to test whether plant foraging strategies, not just individual behaviors, differ among species. This information is critical to ask additional questions regarding the role of behaviors in mediating species interactions and influencing patterns of species coexistence. An alternative approach focuses not on the behavior themselves, but on the resulting patterns of habitat use (e.g., root placement), which are assumed to be the outcome of behavior. Resource selection functions (RSFs) predict the probability that a particular spatial unit will be used by an organism based on numerous factors (e.g., resources, temperature, predators, competitors) present in that spatial unit (Boyce et al. 2002, Lele & Keim 2006, MacKenzie et al. 2002, Manley et al. 2002, McLoughlin et al. 2010). For studies of plant root foraging, data would generally take the form of the presence/absence of roots. Furthermore, researchers could probably be confident that plants are foraging within resource units were roots occur. This is different from many animal studies in which the presence/absence of animals often comes from GPS collars, and presence does not necessarily indicate that the animal was using the habitat (e.g., the animal may have simply been walking through). Consequently, plant behaviorists would typically fit a special case of an RSF called the resource selection probability function (RSPF). The RSPF is estimated by fitting generalized linear models (GLMs) to binary species presence data as a response variable and using the various attributes of spatial units as predictors of habitat choice (Boyce et al. 2002, Lele & Keim 2006, Manley et al. 2002). A GLM fit in this manner generates a linear model that can predict the probability that a microsite containing a particular combination of conditions will be used by an organism: the physical outcome of multiple, interacting, behavioral types. To generate a RSPF, one must measure ecologically important attributes of microsites throughout the potential range of an organism, as well as the presence/absence of that individual within each microsite (Lele & Keim 2006). RSPFs can be calculated based on either www.annualreviews.org • Plant Foraging Behavior Behavioral syndrome: suites of correlated behaviors displayed by individuals or species that are consistent across multiple situations 301 ES42CH14-Cahill ARI 7 October 2011 15:40 Annu. Rev. Ecol. Evol. Syst. 2011.42:289-311. Downloaded from www.annualreviews.org by University of Alberta on 11/29/13. For personal use only. AN EXAMPLE OF A RESOURCE SELECTION FUNCTION TO DESCRIBE PLANT FORAGING Resource selection functions (RSFs) have been used to understand how habitat is used by animals and may provide insight into plant foraging. For example, Cahill et al. (2010) developed generalized linear models to predict soil occupancy by the roots Abutilon theophrasti as a function of soil heterogeneity (patch-center, patch-edge, homogeneous), neighbors (presence/absence of neighbors), and distance from the stem (continuous). Although they did not present the linear model in their paper, the parameter estimates from the model reveal the magnitude and direction of each main and interaction effect on the probability of finding a plant root at each location in the soil. This linear model is equivalent to an RSF (see Supplemental Table 1; follow the Supplemental Material link from the Annual Reviews home page at http://www.annualreviews.org). For example, there was a stronger negative effect on the probability of finding roots in the presence of neighbors when a nutrient patch was on the edge of the mesocosm (estimate of −2.51) than when the patch was in the center of the mesocosm between the two plants (estimate of −0.60), indicating more overlap when the patch was in the center. Supplemental Material 302 the habitat use of individuals or the aggregated use of multiple individuals within a population. In either case, for plant roots this will involve collecting root cores, measuring some cues that are known to influence root placement (e.g., Table 2), and then identifying the presence/absence of target plant species (or individuals) within that root core (Table 1). Although limited in scope, one such model has been generated for plant foraging. As described above, A. theophrasti alters responses to neighbors depending on nutrient heterogeneity (Cahill et al. 2010). This finding emerged when a GLM was fit to binary data as described above (see the sidebar). Importantly, predictions derived from RSFs and RSPFs are developed not by building from first principles, but by modeling observed patterns of habitat use. This is advantageous when there is neither an a priori expectation of how plants respond to particular soil cues nor any expectation that such responses would be consistent among species or locations. We believe that the use of RSFs and RSPFs may be effective for understanding patterns of the habitat occupancy of individual plant roots and for generating descriptive models of the whole-plant foraging strategy. A strength of this approach is that the underlying GLMs can handle many behavioral cues simultaneously, as well as nonadditive interactions (Table 2). A further benefit of GLMs is that information theoretic approaches can be used to simplify the linear models and generate testable hypotheses about which factors are likely the most important under different conditions. RSFs and RSPFs produced from a GLM generate parameter estimates indicating the magnitude and direction of each cue on the probability of finding a root in a particular location. This information will be critical to focus research on only the most important cues that influence root placement in a species, and not on an endless sea of possible soil cues. Finally, the generation of RSFs and RSPFs for multiple species may facilitate interspecific comparisons of nutrient foraging strategies. One of the greatest limitations to the generation of RSFs and RSPFs for understanding the behavioral determinants of root placement is that it has historically been much easier to track an elk or wolf with a GPS collar than to identify a root to a species. As a result, behaviorists have fine-scale and spatially explicit occupancy data for a diversity of animals in remote locations, whereas similar information for plants under natural conditions is generally lacking. However, the development of molecular tools (Table 1) should reduce this limitation. A related limitation is a general lack of small-scale information on microsite conditions, including the distributions of resources and other organisms. The collection of such information presents technological and Cahill · McNickle ES42CH14-Cahill ARI 7 October 2011 15:40 economic challenges, and until these hurdles are cleared, RSFs related to plant foraging will likely be limited to only a small number of potential factors. However, animal behaviorists have shown that when fuller models are generated, there is the potential for great insight into the biology of the organisms studied (Sih et al. 2004a). Annu. Rev. Ecol. Evol. Syst. 2011.42:289-311. Downloaded from www.annualreviews.org by University of Alberta on 11/29/13. For personal use only. Linking Behavioral Strategies to Community Dynamics Interspecific variation in behavioral types and foraging strategies has the potential to influence community processes in a number of ways. For example, if behavioral types cause altered competitive ability under certain conditions, then this represents one potential assembly rule driving community structure. Such community-level consequences have been suggested in reference to interspecific variation in foraging, leading to the idea that competitive outcomes may be contingent on small-scale resource distributions (Bliss et al. 2002, Caldwell et al. 1991b, Campbell et al. 1991, Casper & Cahill 1996, Fransen & de Kroon 2001) and that variation in foraging precision can influence species coexistence (Campbell et al. 1991). However, it has also been suggested that plants may simply average out such small-scale variation, and thus it will have no impact on competitive dynamics and coexistence (Tilman & Pacala 1993). There are not sufficient data to adequately resolve this issue. If behavior results in habitat partitioning (i.e., avoidance), then competition among plants may be reduced, and this may enhance species coexistence (sensu MacArthur 1958). Alternatively, if behavior results in shared habitat use (i.e., aggregation) among neighboring plants, competition Outcome of ecological interactions Individual, population, and community consequences Phenotype (root placement) Plasticity Local conditions Multiple cues Stochasticity Fixed development Genotype Realized behavioral types Realized root foraging strategies Information integration Potential root foraging strategies Potential behavioral types Figure 2 Conceptual model summarizing the main topics presented in this review. In this model, ecological interactions and larger-scale processes ( purple) are influenced by plant phenotype ( pink), such as patterns of root placement. The phenotype is determined by three processes (orange), including plasticity. The expression of plasticity is influenced both by behavioral pathways (blue) and by nonbehavioral responses to local conditions ( green). Behavioral pathways are themselves influenced by genotype (dark gray), which limits the potential behaviors that can be expressed (light gray) as well as fixed developmental pathways (orange). www.annualreviews.org • Plant Foraging Behavior 303 ARI 7 October 2011 15:40 may be increased, limiting species coexistence. As a result, root placement has as much potential to influence species coexistence as the details of how different species of warblers feed within trees (e.g., MacArthur 1958). Unfortunately, the description of patterns of habitat use and microsite conditions of plant roots is substantially more difficult than the challenges faced by an ornithologist collecting similar information on birds foraging in the woods. The collection of such microsite information on habitat use and associated factors for plants remains rare (but see Cahill et al. 2010 and Mommer et al. 2010) and is critical to linking nutrient foraging behavior of plants to community processes. We suggest that the potential impacts of behavior for plant communities and plant coexistence are similar to the roles of other functional traits (McGill et al. 2006) but are understudied. Behavioral strategies need to be more explicitly included in efforts to move from the individual up to the population or community (Figure 2). Through the quantification of both developmentally invariable pathways (those commonly associated with functional traits) and variable pathways (those commonly associated with behavior), there is the possibility of a greater predictive understanding of how traits influence communities. Annu. Rev. Ecol. Evol. Syst. 2011.42:289-311. Downloaded from www.annualreviews.org by University of Alberta on 11/29/13. For personal use only. ES42CH14-Cahill CONCLUSIONS Above we show that many aspects of the soil environment act as cues, inducing alternative behavioral types in plants. For a given cue, species vary in their behavioral type (Figure 1 and Table 2), although data are lacking for many factors that influence plant behavior. We also demonstrate that responses to multiple cues are nonadditive and are highly context dependant. Consequently, the resulting behaviors are not always predictable from the behavior of plants faced with each cue individually (Bliss et al. 2002, Cahill et al. 2010, Hodge et al. 1999, Robinson et al. 1999). Owing to the contingent nature of plant foraging behavior, we suggest that a multi-dimensional representation of multiple behaviors defines a plant’s foraging strategy. Drawing from the work of animal behaviorists, we suggest that behavioral syndromes and resource selection functions may provide descriptive models of how plants occupy the soil environment. Such models can lead to an increased understanding of possible plant responses and guide the development of predictive models. When combined with modern molecular tools for the identification of roots to species, these statistical tools may lead to the identification of cues and behaviors that most commonly influence patterns of habitat use among co-occurring individuals. Differences in habitat use due to variation in behavioral strategies may impact species coexistence. We encourage the integration of foraging strategies with more traditional research on plant functional traits and resource strategies (Figure 2). This combined approach has the potential to enhance our understanding of both the functional ecology of plants and the processes that drive larger-scale processes. There is substantial opportunity for future research in nearly all aspects of plant foraging behavior, although we highlight several areas in particular need of further research. FUTURE ISSUES 1. Forde (2009) introduced developmental instability as a concept that involves stochastic processes shaping root growth and development. Genetic variation in the degree of instability expressed suggests this stochasticity may have an adaptive role in nutrient foraging. There is a need to quantify stochasticity for more taxa and determine its role in plant foraging. The application of the existing theory of animal movement (e.g., Levy walk; Humphries et al. 2010) to root growth may provide insights into nutrient foraging strategies. 304 Cahill · McNickle ES42CH14-Cahill ARI 7 October 2011 15:40 Annu. Rev. Ecol. Evol. Syst. 2011.42:289-311. Downloaded from www.annualreviews.org by University of Alberta on 11/29/13. For personal use only. 2. More theoretical and empirical work is needed to understand how plants respond to neighbor roots. Few studies have been able to differentiate between behavioral responses (no response, avoidance, aggregation) and competitive suppression (e.g., allelopathy). Future studies should include a diversity of taxa, allowing for phylogenetic comparison. Early theoretic work focused primarily on intraspecific competition among annual plants. Additional theory is needed that accounts for a greater diversity of plants (e.g., interspecific competition or perennials) in combination with additional soil factors (e.g., predators, nutrient heterogeneity), and this should draw on a diversity of theoretical approaches. 3. Studies documenting behavioral responses to microbial competitors are nearly nonexistent. Given the ubiquitous nature of plant-microbial interactions in natural systems, this is a substantial gap in our current understanding. A first step would be to determine what behavioral types exist, such as no response, avoidance, and aggregation. 4. Similar to microbial competitors, there are few studies or theories describing how root enemies (e.g., predators and pathogens) alter plant root placement strategies. In contrast, trade-offs between predation risk and foraging reward are among the most classic examples of contingencies in behavioral type (Lima 1998). We suggest that roots need to be directly observed through time (e.g., minirhizotron cameras or other methods), allowing for differentiation between root removals by herbivores (root deaths) compared with direct behavioral adjustments of root placement (root births). 5. Mycorrhizal fungi draw significant resources from their host plants, and the host plants draw a variety of resources from the fungi. Viewing this relationship as reciprocal parasitism explicitly recognizes conflicts of interest among the partners, with uncertain implications for the consequences of differential behavioral types in the host plant. Furthermore, it is common in animal host-parasite systems for the parasite to manipulate host behavior, typically in a way that benefits the parasite. Whether this is common in plant-fungal symbiosis is unknown. More data are needed describing whether plants alter behavior as a function of fungal colonization from a diversity of species. Furthermore, the consequences of differential behavioral types for both the host and the fungi are completely lacking and are critical to understanding the behavioral ecology of this interaction. 6. Most studies of plant root foraging have focused on nutrients as a group. This likely resembles some natural situations in which there can be high spatial covariance among concentrations of different resources (e.g., urine patch). However, other situations may result in the elevation of specific nutrients. How plants respond to specific nutrients is poorly understood, particularly if different resources vary at different locations within a single root system. Furthermore, despite water being critical for all plants, it has been greatly understudied in the context of root foraging relative to its biological importance. 7. Animal behaviorists have long lists of behaviors that are expressed in the context of foraging and movement (e.g., ethograms). Plant ecologists do not and instead tend to rely on root placement or foraging precision as representative of plant foraging behavior. Plants need to be screened for additional, repeatable aspects of foraging behavior. This needs to be done with large numbers of taxa, investigating intra- and interspecific variability. www.annualreviews.org • Plant Foraging Behavior 305 ES42CH14-Cahill ARI 7 October 2011 15:40 8. Additional empirical studies are needed that combine multiple environmental cues to determine how plant behavior is adjusted in the presence of multiple cues. Such data need to be effectively synthesized into a usable form; resource selection functions may be a possible tool, although there exist many alternatives in animal behavior and niche modeling. Theory in this area is also underdeveloped, and concepts such as behavioral syndromes should be considered. Annu. Rev. Ecol. Evol. Syst. 2011.42:289-311. Downloaded from www.annualreviews.org by University of Alberta on 11/29/13. For personal use only. 9. There are few conceptual or empirical studies that link foraging strategies to larger patterns of communities and populations. Particularly rare are field studies, and instead the focus has been on greenhouse and mesocosm experiments. An initial step would be to develop RSPFs from in situ plant communities, helping to identify consistent cues that influence root placement. This information can be used to design field experiments. DISCLOSURE STATEMENT The authors are not aware of any affiliations, memberships, funding, or financial holdings that might be perceived as affecting the objectivity of this review. ACKNOWLEDGMENTS We thank many colleagues for extensive discussions leading to the ideas presented here, including Mark Boyce, Hans de Kroon, Liesje Mommer, Colleen St. Clair, Pete Hurd, Joel S. Brown, and Cam Wild. We thank Pamela Belter, Brenda Casper, Hans de Kroon, Robert Jones, Justine Karst, Liesje Mommer, Samson Nyanumba, and Rob Jackson for feedback on earlier drafts. This work was funded by an NSERC Discovery Grant and Accelerator Supplement awarded to J.F.C. LITERATURE CITED Baluska F, Lev-Yadun S, Mancuso S. 2010. Swarm intelligence in plant roots. Trends Ecol. Evol. 25:682–83 Bilbrough CJ, Caldwell MM. 1995. The effect of shading and N status on root proliferation in nutrient patches by the perennial grass Agropyron desertorum in the field. Oecologia 103:10–16 Bliss KM, Jones RH, Mitchell RJ, Mou PP. 2002. Are competitive interactions influenced by spatial nutrient heterogeneity and root foraging behavior? New Phytol. 154:409–17 Bobowski BR, Hole D, Wolf PG, Bryant L. 1999. 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Root system size and precision in nutrient foraging: responses to spatial pattern of nutrient supply in six herbaceous species. J. Ecol. 89:972–83 www.annualreviews.org • Plant Foraging Behavior 311 ES42-FrontMatter ARI 11 October 2011 16:5 Annu. Rev. Ecol. Evol. Syst. 2011.42:289-311. Downloaded from www.annualreviews.org by University of Alberta on 11/29/13. For personal use only. Contents Annual Review of Ecology, Evolution, and Systematics Volume 42, 2011 Native Pollinators in Anthropogenic Habitats Rachael Winfree, Ignasi Bartomeus, and Daniel P. Cariveau p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 1 Microbially Mediated Plant Functional Traits Maren L. Friesen, Stephanie S. Porter, Scott C. Stark, Eric J. von Wettberg, Joel L. Sachs, and Esperanza Martinez-Romero p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p23 Evolution in the Genus Homo Bernard Wood and Jennifer Baker p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p47 Ehrlich and Raven Revisited: Mechanisms Underlying Codiversification of Plants and Enemies Niklas Janz p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p71 An Evolutionary Perspective on Self-Organized Division of Labor in Social Insects Ana Duarte, Franz J. Weissing, Ido Pen, and Laurent Keller p p p p p p p p p p p p p p p p p p p p p p p p p p p p91 Evolution of Anopheles gambiae in Relation to Humans and Malaria Bradley J. White, Frank H. Collins, and Nora J. Besansky p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 111 Mechanisms of Plant Invasions of North America and European Grasslands T.R. Seastedt and Petr Pyšek p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 133 Physiological Correlates of Geographic Range in Animals Francisco Bozinovic, Piero Calosi, and John I. Spicer p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 155 Ecological Lessons from Free-Air CO2 Enrichment (FACE) Experiments Richard J. Norby and Donald R. Zak p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 181 Biogeography of the Indo-Australian Archipelago David J. Lohman, Mark de Bruyn, Timothy Page, Kristina von Rintelen, Robert Hall, Peter K.L. Ng, Hsi-Te Shih, Gary R. 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McNickle p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 289 Climate Relicts: Past, Present, Future Arndt Hampe and Alistair S. Jump p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 313 Annu. Rev. Ecol. Evol. Syst. 2011.42:289-311. Downloaded from www.annualreviews.org by University of Alberta on 11/29/13. For personal use only. Rapid Evolutionary Change and the Coexistence of Species Richard A. Lankau p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 335 Developmental Patterns in Mesozoic Evolution of Mammal Ears Zhe-Xi Luo p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 355 Integrated Land-Sea Conservation Planning: The Missing Links Jorge G. Álvarez-Romero, Robert L. 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